Biology subjects

DAI, S.

Publications and source records attributed to DAI, S..

4 recordsLinked to original sources

bioRxiv2024

Functional, Biotinylproteomic and Bioinformatic Analysis of Both Cytoskeletal and Plastoskeletal Proteins in Plant Mechanoresponse

To investigate the early signaling components of skeletal proteins in mediating Arabidopsis thigmomorphogenesis, both microscopic and proximity labeling (PL)-based quantitative biotinylproteomics were applied to investigate the subcellular location and putative interactors of a touch-responsive WPRa4 protein. These experiments have demonstrated that the cytoskeletal protein WPRa4 is localized nearby the plastid. Several cytosolic Plastid Movement-Impaired (PMI) proteins and a member of the plastidic translocon were identified as putative interactors of WPRa4, suggesting an integrated network of skeletal proteins linking the cytoskeleton with the plastid membrane. Further bioinformatic analysis of both Proximity Labeling- and XL-MS-based proteomic results suggested that Plastid Movement-Impaired 4 (PMI4) protein may serve as a candidate in mediating the plant touch response. The loss-of-function pmi4 mutant showed neither the touch-induced bolting delay nor the rosette size reduction upon repetitive touches, suggesting that pmi4 is a unique type of mutant of Arabidopsis thigmomorphogenesis. Moreover, the null mutant pmi4 displayed a severe defect in the touch-induced Ca2+ oscillation. Further transcriptomic analysis performed on both the wild-type Arabidopsis and pmi4 mutant indicated that the mutated pmi4 gene suppressed the expression of a number of touch rapidly induced transcripts and a JA-responsive gene, LOX2. These findings led us to propose a revised touch force-sensing theory, in which the interconnected cytosolic and plastidic skeletal proteins serve as the early mechano-sensing components mediating Arabidopsis thigmomorphogenesis and the retrograde calcium signaling in response to touch.

plant biology↗

bioRxiv2024

Profiling amino acid substitution on proteomic scale unveils novel protein regulation in cancer diseases

Amino acid (AA) substitutions play a critical role in regulating cellular activities, including complex signaling and cell cycle processes. Recent research on AA substitutions has primarily relied on genomic and transcriptomic data. The identification at the proteomic scale remains underexplored, despite evidence suggesting that DNA and RNA biosynthesis are not the sole sources of these substitutions. This gap persists due to challenges in analyzing large-scale proteomic data. In this study, we address this limitation by analyzing multiple independent datasets across five cancer types using PIPI-C, a novel mass spectrometry data analysis tool. And we propose AA substitutomics, a pipeline for characterizing AA substitutions arising after protein translation and dissecting the regulatory functions of key proteins with AA substitutions. Among our identified AA substitutions, 87% are novel findings and not recorded in genomic/transcriptomic databases, which indicates that the post-translational AA substitutions are prevalent. Our findings reveal biologically significant AA substitutions linked to cancer, such as F43S and E91D in hemoglobin subunit beta, P584T in filamin A, and A175N in fructose-bisphosphate aldolase B. Furthermore, our pipeline enables direct investigation of drug resistance and immune escape. By capturing functional protein-level alterations beyond genomic and transcriptomic profiling, it establishes a robust framework to advance cancer research.

cancer biology↗

bioRxiv2024

Identifying crosstalks among post-translational modifications in lung cancer proteomic data

Post-translational modifications (PTMs) are pivotal in cellular regulations, and their crosstalk is related to various diseases such as cancer. Given the prevalence of PTM crosstalk within close amino acid ranges, identifying peptides with multiple PTMs is essential. However, this task is an NP-hard combinatorial problem with exponential complexity, posing significant challenges for existing analysis methods. Here, we introduce PIPI-C (PTM-Invariant Peptide Identification with a Combinatorial model), a novel search engine that addresses this challenge through a mixed-integer linear programming (MILP) model, thereby overcoming the limitations of existing approaches that struggle with high-order PTM combinations. Rigorous validation across diverse datasets confirms PIPI-Cs superior performance in detecting PTM crosstalks. When applied to over 72 million mass spectra of three human cancers--lung squamous cell carcinoma (LSCC), colorectal adenocarcinoma (COAD), and glioblastoma (GBM)--PIPI-C reveals significantly upregulated PTM crosstalks. In LSCC, 50% of 860 upregulated unique PTM site patterns (UPSPs) (when comparing cancer vs. normal samples) carried at least two PTMs, including literature-supported crosstalks such as di-methylation with trifluoroleucine substitution and amidation with proline-to-valine substitution. Similar findings in COAD and GBM highlight PIPI-Cs utility in uncovering cancer-relevant PTM crosstalk landscapes. Overall, PIPI-C provides a robust mathematical framework for decoding complex PTM patterns, advancing our understanding of PTM-driven cellular processes in diseases.

bioinformatics↗

bioRxiv2024

Searching Post-translational Modifications in Cross-linking Mass Spectrometry Data

Cross-linking mass spectrometry (XL-MS) is a technique for investigating protein-protein interactions (PPIs) and protein structures. In the realm of biology, post-translational modifications (PTMs) play a critical role in regulating PPIs and reshaping protein structures. However, the identification of PTMs in XL-MS data poses a great computational challenge and thus remains unexplored. In this study, we introduce SeaPIC, the first XL-MS tool that enables biologists to investigate PTMs in PPIs and protein structures. Our experiments demonstrate the successful identification of PTMs within cross-linked peptides, which were previously undiscovered.

bioinformatics↗